Biological health is not a single, stable fixed point. Rather, health reflects a rich interplay of complex dynamics. Recent observations suggest that erosion of mechanisms that underlie natural physiological complexity may be one of the most significant damaging effects of trauma or illness. To preempt that erosion, there is a critical need for predictive physiology. To date, clinical predictions are based on pattern classification. Echoing predictive meteorology-that is, the use of dense data, high speed computing and repeated application of simple physical law-this project embarks on predictive physiology. This marriage of the mathematics and clinical medicine contains a deeper postulate, the existence of """"""""Newton's Laws for Biology."""""""" Modern biology embraces principles of scaling, modularity, and heritability. These principles speak to structure, distance and space. The team believes that there is underlying structure to biological time and observables. With its access to dense data, computational power, and far-from-equilibrium theory, the team will explore specific clinically important contexts. The team will develop, advance, and apply the conceptual framework of fluctuation-dissipation theory to predict the response to standard clinical interventions from the fluctuations that characterize all physiologic time series data. The model intervention is the spontaneous breathing trial (SBT), a frequent procedure in critical care during which mechanical ventilation is briefly suspended while the patient breathes for a period of time without that support. Clinical data from Emory University Hospitals will be used to test these novel far-from-equilibrium predictions for the heart rate and blood pressure responses. As a conservative step toward improving treatment, the team will also predict the decisions of clinicians following SBTs, namely whether groups of patients will be safely be liberated from the ventilator.
In this project, a conceptual framework from mathematics will be developed and applied to predict how groups of critically ill patients respond to treatment. It is anticipated that the proposed FDT approach will also be broadly applicable for predicting the response of patients to physiologic stress or clinical interventions. This applicability will be increasingly valuable as additional time-dependent data are collected.
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